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(Excerpt from original post on the Taneja Group News Blog)

I’ve been immersed in “Open” for the last two weeks here in Boston, attending both Red Hat Summit 2017 and then OpenStack Summit. There are quite a few things worth paying attention to, especially if you are an enterprise IT shop still wondering how your inevitable cloud (and services) transformation is really going to play out, including accelerating application migration to containers and the rise of platform Management as a Service.

An IT industry analyst article published by SearchDataCenter.

Emerging technologies such as containers, HCI and big data have blurred the lines between compute and storage platforms, breaking down traditional IT silos.

Mike Matchett

With the rise of software-defined storage, in which storage services are implemented as a software layer, the whole idea of data storage is being re-imagined. And with the resulting increase in the convergence of compute with storage, the difference between a storage platform and a data-processing platform is further eroding.

Storage takes new forms

Let’s look at a few of the ways that storage is driving into new territory:

Now in containers! Almost all new storage operating systems, at least under the hood, are being written as containerized applications. In fact, we’ve heard rumors that some traditional storage systems are being converted to containerized form. This has a couple of important implications, including the ability to better handle massive scale-out, increased availability, cloud-deployment friendliness and easier support for converging computation within the storage.

Merged and converged. Hyper-convergence bakes software-defined storage into convenient, modular appliance units of infrastructure. Hyper-converged infrastructure products, such as those from Hewlett Packard Enterprise’s SimpliVity and Nutanix, can greatly reduce storage overhead and help build hybrid clouds. We also see innovative approaches merging storage and compute in new ways, using server-side flash (e.g., Datrium), rack-scale infrastructure pooling (e.g., Drivescale) or even integrating ARM processors on each disk drive (e.g., Igneous).

Bigger is better. If the rise of big data has taught us anything, it’s that keeping more data around is a prerequisite for having the opportunity to mine value from that data. Big data distributions today combine Hadoop and Spark ecosystems, various flavors of databases and scale-out system management into increasingly general-purpose data-processing platforms, all powered by underlying big data storage tools (e.g., Hadoop Distributed File System, Kudu, Alluxio).

Always faster. If big is good, big and fast are even better. We are seeing new kinds of automatically tiered and cached big data storage and data access layer products designed around creating integrated data pipelines. Many of these tools are really converged big data platforms built for analyzing big and streaming data at internet of things (IoT) scales.

The changing fundamentals

Powering many of these examples are interesting shifts in underlying technical capabilities. New data processing platforms are handling more metadata per unit of data than ever before. More metadata leads to new, highly efficient ways to innovate …(read the complete as-published article there)

An IT industry analyst article published by SearchStorage.

The amount of data available to today’s enterprise is staggering. Yet the race to collect and mine even more data to gain competitive insight, deeply optimize business processes and better inform strategic decision-making is accelerating. Fueled by these new data-intensive capabilities, traditional enterprise business applications primarily focused on operational transactions are now quickly converging with advanced big data analytics to help organizations grow increasingly (albeit artificially) intelligent.

To help IT keep pace with data-intensive business applications that are now embedding operational analytics, data center infrastructure is also evolving rapidly. In-memory computing, massive server-side flash, software-defined resources and scale-out platforms are a few of the recent growth areas reshaping today’s data centers. In particular, we are seeing storage infrastructure, long considered the slow-changing anchor of the data center, transforming faster than ever. You might say that we’re seeing smarter storage.

Modern storage products take full advantage of newer silicon technologies, growing smarter with new inherent analytics, embedding hybrid cloud tiering and (often) converging with or hosting core data processing directly. Perhaps the biggest recent change in storage isn’t with hardware or algorithms at all, but with how storage can now best be managed.

For a long time, IT shops had no option but to manage storage by deploying and learning a unique storage management tool for each type of vendor product in use. This wastes significant time implementing, integrating and supporting one-off instances of complex vendor-specific management tools. But as managing data about business data (usage, performance, security and so on, see “Benefits of analytical supercharging”) grows, simply managing a metrics database now becomes a huge challenge as well. Also, with trends like the internet of things proliferating the baking of streaming sensors into everything, key systems metadata is itself becoming much more prolific and real-time.

It can take a significant data science investment to harvest the desired value out of it.

Perhaps the biggest recent change in storage isn’t with hardware or algorithms at all, but with how storage can now best be managed.

Storage analytics ‘call home’

So while I’m all for DIY when it comes to unique integration of analytics with business processes and leveraging APIs to create custom widgets or reports, I’ve seen too many enterprises develop their own custom in-house storage management tools, only for those eventually becoming as expensive and onerous to support and keep current as if they had just licensed one of those old-school “Big 4” enterprise management platforms (i.e., BMC, CA, Hewlett Packard Enterprise [HPE] and IBM). In these days of cloud-hosted software as a service (SaaS) business applications, it makes sense that such onerous IT management tasks should be subscribed out to and provided by a remote expert service provider.

Remote storage management on a big scale really started with the augmented vendor support “call home” capability pioneered by NetApp years ago. Log and event files from on-premises arrays are bundled up and sent daily back to the vendor’s big data database “in the cloud.” Experts then analyze incoming data from all participating customers with big data analysis tools (e.g., Cassandra, HBase and Spark) to learn from their whole pool of end-user deployments.
Benefits of analytical supercharging

Smarter infrastructure with embedded analytical intelligence can help IT do many things better, and in some cases even continue to improve with automated machine learning. Some IT processes already benefitting from analytical supercharging include the following:

Troubleshooting. Advanced analytics can provide predictive alerting to warn of potential danger in time to avoid it, conduct root cause analyses when something does go wrong to identify the real problem that needs to be addressed and make intelligent recommendations for remediation.

Resource optimization. By learning what workloads require for good service and how resources are used over time, analytics can help tune and manage resource allocations to both ensure application performance and optimize infrastructure utilization.

Operations automation. Smarter storage systems can learn (in a number of ways) how to best automate key processes and workflows, and then optimally manage operational tasks at large scale — effectively taking over many of today’s manual DevOps functions.

Brokerage. Cost control and optimization will become increasingly important and complex as truly agile hybrid computing goes mainstream. Intelligent algorithms will be able to make the best cross-cloud brokering and dynamic deployment decisions.

Security. Analytical approaches to securing enterprise networks and data are key to processing the massive scale and nonstop stream of global event and log data required today to find and stop malicious intrusion, denial of service and theft of corporate assets.

That way, the array vendor can deliver valuable proactive advice and recommendations based on data any one organization simply couldn’t generate on its own. With this SaaS model, IT doesn’t have to manage their own historical database, operate a big data analysis platform or find the data science resources to analyze it. And the provider can gain insight into general end-user behavior, study actual feature usage and identify sales and marketing opportunities.

Although it seems every storage vendor today offers call home support, you can differentiate between them. Some look at customer usage data at finer-grained intervals, even approaching real-time stream-based monitoring. Some work hard on improving visualization and reporting. And others intelligently mine collected data to train machine learning models and feedback smarter operational advice to users…(read the complete as-published article there)

(Excerpt from original post on the Taneja Group News Blog)

What’s a Cloud Converged system? It is really what us naive people thought hybrid storage was all about all along. Yet until now no high performance enterprise class storage ever actually delivered it. But now, Oracle’s latest ZFS Storage Appliance, the ZS5, comes natively integrated with Oracle Cloud storage. What does that mean? On-premise ZS5 Storage Object pools now extend organically into Oracle Cloud storage (which is also made up of ZS storage) – no gateway or third party software required.

Oracle has essentially brought enterprise hybrid cloud storage to market, no integration required. I’m not really surprised that Oracle has been able to roll this out, but I am a little surprised that they are leading the market in this area.

Why hasn’t Dell EMC come up with a straightforward hybrid cloud leveraging their enterprise storage and cloud solutions? Despite having all the parts, they failed to actually produce the long desired converged solution – maybe due to internal competition between infrastructure and cloud divisions? Well, guess what. Customers want to buy hybrid storage, not bundles or bunches of parts and disparate services that could be integrated (not to mention wondering who supports the resulting stack of stuff).

Some companies so married to their legacy solutions that they, like NetApp for example, don’t even offer their own cloud services – maybe they were hoping this cloud thing would just blow over? Maybe all those public cloud providers would stick with web 2.0 apps and wouldn’t compete for enterprise GB dollars?

(Microsoft does have StorSimple which may have pioneered on-prem storage integrated with cloud tiering (to Azure). However, StorSimple is not a high performance, enterprise class solution (capable of handling PBs+ with massive memory accelerated performance). And it appears that Microsoft is no longer driving direct sales of StorSimple, apparently positioning it now only as one of many on-ramps to herd SME’s fully into Azure.)

We’ve reported on the Oracle ZFS Storage Appliance itself before. It has been highly augmented over the years. The Oracle ZFS Storage Appliance is a great filer on its own, competing favorably on price and performance with all the major NAS vendors. And it provides extra value with all the Oracle Database co-engineering poured into it. And now that it’s inherently cloud enabled, we think for some folks it’s likely the last storage NAS they will ever need to invest in (if you’ll want more performance, you will likely move to in-memory solutions, and if you want more capacity – well that’s what the cloud is for!).

Oracle’s Public Cloud is made up of – actually built out of – Oracle ZFS Storage Appliances. That means the same storage is running on the customer’s premise as in the public cloud they are connected with. Not only does this eliminate a whole raft of potential issues, but solving any problems that might arise is going to be much simpler – (and less likely to happen given the scale of Oracle’s own deployment of their own hardware first).

Compare this to NetApp’s offering to run a virtual image of NetApp storage in a public cloud that only layers up complexity and potential failure points. We don’t see many taking the risk of running or migrating production data into that kind of storage. Their NPS co-located private cloud storage is perhaps a better offering, but the customer still owns and operates all the storage – there is really no public cloud storage benefit like elasticity or utility pricing.

Other public clouds and on-prem storage can certainly be linked with products like Attunity CloudBeam, or additional cloud gateways or replication solutions. But these complications are exactly what Oracle’s new offering does away with.

There is certainly a core vendor alignment of on-premises Oracle storage with an Oracle Cloud subscription, and no room for cross-cloud brokering at this point. But a ZFS Storage Appliance presents no more technical lock-in than any other NAS (other than the claim that they are more performant at less cost, especially for key workloads that run Oracle Database.), nor does Oracle Cloud restrict the client to just Oracle on-premise storage.

And if you are buying into the Oracle ZFS family, you will probably find that the co-engineering benefits with Oracle Database (and Oracle Cloud) makes the set of them all that much more attractive (technically and financially). I haven’t done recent pricing in this area, but I think we’d find that while there may be cheaper cloud storage prices per vanilla GB out there, looking at the full TCO for an enterprise GB, hybrid features and agility could bring Oracle Cloud Converged Storage to the top of the list.

RT @TruthinIT: There's no cost of goods like a traditional NAS device where I've got disks I've got to pay for. And if I'm not using the data on those disks, I still got to pay for those disks. bit.ly/2BBX073@Nasuni@smworldbigdata

In 30 min I'm interviewing @Cohesity (and customer) on @TruthinIT about Mass Data Fragmentation. It's about having too many copies in about four or five different "dimensions", including cloud! Join us webcast (12.11.18) @ 1pmET (and there will be prizes) bit.ly/2PdqrQn